Process window signature patterns for lithography process control

ABSTRACT

A method for identifying process window signature patterns in a device area of a mask is disclosed. The signature patterns collectively provide a unique response to changes in a set of process condition parameters to the lithography process. The signature patterns enable monitoring of associated process condition parameters for signs of process drift, analyzing of the process condition parameters to determine which are limiting and affecting the chip yields, analyzing the changes in the process condition parameters to determine the corrections that should be fed back into the lithography process or forwarded to an etch process, identifying specific masks that do not transfer the intended pattern to wafers as intended, and identifying groups of masks that share common characteristics and behave in a similar manner with respect to changes in process condition parameters when transferring the pattern to the wafer.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.12/660,313, filed on Feb. 23, 2010, and issued as U.S. Pat. No.8,057,967, on Nov. 15, 2011, which is a divisional of U.S. patentapplication Ser. No. 11/466,978, filed on Aug. 24, 2006, and issued asU.S. Pat. No. 7,695,876, on Apr. 13, 2010, which claims priority fromU.S. Provisional Patent Application No. 60/713,123, filed on Aug. 31,2005, the contents of all the applications being hereby incorporated byreference.

FIELD OF THE INVENTION

This invention relates generally to lithography processes and relatesmore particularly to a method for identifying process window signaturepatterns for lithography process control.

BACKGROUND

In the semiconductor industry, microlithography (or simply lithography)is the process of printing the circuit patterns on a semiconductor wafer(for example, a silicon or gallium arsenide wafer). Currently, opticallithography is the predominant technology used in volume semiconductormanufacturing. Optical lithography employs light in the visible to deepultraviolet spectrum range to expose the resist on a wafer. In thefuture, extreme ultraviolet (EUV) and soft x-rays may be employed.Following exposure, the resist is developed to yield a relief image.

In optical lithography, a photomask (often called a mask or a reticle)is first written using electron-beam or laser-beam direct-write tools. Atypical mask for optical lithography consists of a glass (or quartz)plate of six to eight inches on a side, with one surface coated with athin metal layer (for example, chrome) of a thickness of about 100 nm.The chip pattern is etched into the metal layer, hence allowing light totransmit through. The area where the metal layer is not etched awayblocks light transmission. In this way, a pattern may be projected ontoa semiconductor wafer.

The mask contains certain patterns and features that are used to createdesired circuit patterns on a wafer. The tool used in projecting themask image onto a wafer is called a stepper or scanner (hereinaftercollectively called “exposure tool”). FIG. 1 is a block diagram of anoptical projection lithographic system 10 of a conventional stepperincluding an illumination source 12, an illumination pupil filter 14, alens subsystem 16 a-c, a mask 18, a projection pupil filter 20, and awafer 22 on which the aerial image of mask 18 is projected.

Illumination source 12 may be laser source operated, for example, at UV(ultra-violet) or DUV (deep ultra-violet) wavelengths. The light beam isexpanded and scrambled before it is incident on illumination pupil 14.Illumination pupil 14 may be a simple round aperture, or havespecifically designed shapes for off-axis illumination. Off-axisillumination may include, for example, annular illumination (i.e., thepupil is a ring with a designed inner and outer radius), quadrupleillumination (i.e., the pupil has four openings in the four quadrant ofthe pupil plane), and other shapes like dipole illumination.

After illumination pupil 14, the light passes through the illuminationoptics (for example, lens subsystem 16 a) and is incident on mask 18.Mask 18 contains the circuit pattern to be imaged on wafer 22 by theprojection optics. As the desired pattern size on wafer 22 becomessmaller and smaller, and those patterns becomes closer and closer toeach other, the lithography process becomes more challenging. In aneffort to improve imaging quality, current processing techniques employresolution enhancement technologies (“RET”), such as, for example,optical proximity correction (“OPC”), phase shift masks (“PSM”),off-axis illumination (“OAI”), condenser and exit pupil filters, and soon.

Many of the RET technologies are applied on or directly to mask 18. Forexample, OPC and PSM, which modify the light wave to (1) compensate forthe imperfection of the imaging property of the projection optics, forexample, the OPC technology is used to compensate the optical proximityeffect due to light interference, and/or (2) take advantage of designedlight interferences to enhance the imaging quality, for example, thephase shift mask technology is used to create phase shifting betweenneighboring patterns to enhance resolution.

Notably, mask 18 may not be “perfect” due to its own manufacturingprocess. For example, corners on mask 18 may not be sharp but may berounded and/or the linewidth may have a bias from design value where thebias may also depend on the designed linewidth value and neighboringpatterns. These imperfections on mask 18 may affect the final imagingquality.

The projection optics (for example, lens subsystems 16 b and 16 c, andprojection pupil filter 20) images mask 18 onto wafer 22. In thisregard, the projection optics includes a projection pupil filter 20.Projection pupil filter 20 limits the maximum spatial frequency of themask pattern that can be passed through the projection optics. A numbercalled “numerical aperture” or NA often characterizes projection pupilfilter 20. There are also proposed RET techniques that modify projectionpupil filter 20, which is generally called pupil filtering. Pupilfiltering may include modulation for both the amplitude and the phase onthe passing light beams.

Due to the wavelength of light being finite, and current techniquesemploying wavelengths that are larger than the minimum linewidth that isprinted on wafer 22, there are typically significant light interferenceand diffractions during the imaging process. The imaging process is nota perfect replication of the pattern on mask 18. Current techniquesemploy physical theory to model this imaging process. Further, due tothe high NA value of current exposure tools, different polarizations ofthe light provide different imaging properties. To more accurately modelthe lithography process, a vector-based model may be used.

The projection optics may be diffraction-limited. However, lenssubsystem 16 b and 16 c in the projection optics are most often notcompletely “perfect.” These imperfections may be modeled as aberrations,which are often abstracted as some undesired phase modulation at theplane of projection pupil filter 20, and are often represented by a setof Zernike coefficients. After the light finally reaches the surface ofwafer 22, they will further interact with the coatings on wafer 22 (forexample, the photo-resist). In this regard, different resist thickness,different optical properties of the resist (for example, its refractiveindex), and different material stack under the resist (for example,bottom-anti-reflection-coating or BARC), may further affect the imagingcharacteristics. Some of these effects may also be abstracted by amodulation at the pupil plane.

When the resist is exposed by the aerial image and thereafter baked anddeveloped, the resist tends to undergo complex chemical and physicalchanges. First principle and empirical models have been developed tosimulate these processes.

When wafers are printed using an exposure tool, ideally, wafer 22 shouldbe placed exactly at the focal plane of the projection optics, or adesignated location away from the focal plane. However, due to theimperfect mechanical control of exposure tools, there is always a smalldeviation between the designated plane and the actual wafer plane. Thatdeviation is called defocus, or sometimes just called “focus,” andrepresented by a distance unit, e.g., 50 nm. The defocus introducesadditional imperfections in the imaging path, and can also becharacterized by a phase modulation at the pupil plane.

Furthermore, all wafers, and dice on the wafers, are ideally exposed bya designated amount of exposure dose, e.g., 20 mJ/cm². However, due tothe imperfections in the illumination control and changes in thereflectivity and uniformity of the film stacks on the wafers beingpatterned, there is always a small deviation between the ideal exposuredose and the actual exposure dose deposited on a die. That deviation iscalled exposure dose variation, or sometimes just called “exposure,” andis represented by a percentage deviation from the ideal exposure dose,e.g., 10%.

The combination of focus and exposure errors means that the dimensionsof the wafer features that are patterned may not match exactly with thedimensions required by the design. Since every structure respondsdifferently to focus and exposure errors in different ways depending onthe width, shape, and local environment of the specific structure, it isnot possible to describe the response of every individual structure in acircuit pattern with a limited set of figures of merit.

In practice, the most common metric used to characterize a lithographyprocess is the width of the smallest features being patterned on a givenprocess layer for a given technology. This minimum dimension is referredto as the “critical dimension” or CD. While critical dimensions areactually intended to represent the three dimensional resist profile, theterm CD is usually associated with a one-dimensional slice through theresist line, also referred to as the linewidth. In a looser definition,the term CD is often used to refer to any linewidth measurement even ifit is not the minimum dimension on the device.

Under a certain lithographic setting (e.g., exposure tool, wavelength,NA, and so on), the amount of defocus and exposure dose variations thata circuit design can tolerate, while still producing functional chips,is called the design's “process window.” The process window is oftencharacterized as an area or region in the two-dimensional F-E plot,where “F” is defocus, and “E” is exposure dose variation. Such a plotmay also be referred to as an “ED tree”, so-called because of thetree-shaped process window which results when exposure (E) is plotted onthe x-axis and defocus (D) is plotted on the y-axis. Some RET techniques(e.g., PSM, scattering bars in OPC, OAI) can enhance the process windowfor a certain circuit design. The settings of the exposure tool (e.g.,NA) also have a big impact on the process window.

FIG. 2 shows one example of a process window 30 in the F-E plane. When awafer is exposed in a F-E condition within process window 30, the chipsmade from dice on that wafer will be functional. When the wafer isexposed in a F-E condition outside process window 30, the chips from thewafer will not be functional. The larger process window 30, the morerobust the circuit design is, and will have higher yield inmanufacturing.

A process window for a lithography process is further limited becausedifferent patterns within a circuit design have different processwindows. These differences may include shifts in the best focal planeposition, shifts in the best exposure, and changes in the allowed rangeof focus and exposure. Different patterns also have different criteriafor successful printing. While CD variation of up to +/−15% may betolerable for some non-critical features, the tolerance for the mostcritical structures may be only half as much. The failure modes ofdifferent patterns may also be very different. Some patterns may beconsidered unacceptable due to excessive CD variation, others due toexcessive changes in their sidewall profiles, and others may suffer fromexcessive line end pullback or corner rounding. Catastrophic patternfailures are also possible due to the interaction of neighboringstructures. A CD variation that may be acceptable if a given featurewere being printed as an isolated structure might cause bridging,necking, or other unacceptable pattern variations in a different localenvironment.

The ultimate goal of the lithography process is to deliver a robust andwell maintained “common process window,” that is, a process window inwhich every feature prints within specifications. An example of theoverlapping of multiple process windows for individual structuresleading to a common process window is shown in FIG. 3. The moredifferent types of structures one desires to print with acceptablepattern fidelity in a single exposure, the more individual processwindows need to overlap successfully and the tighter the overlappingcommon process window usually becomes. This common process window mayalso be plotted as a series of overlapping ED trees, and has beenreferred to in the literature as an “ED forest.”

OPC is one of a number of techniques available to lithography processdesigners to optimize the process window overlap between all criticalfeatures by adjusting the mask level pattern so that, in principle, allpatterns are reproduced at the desired wafer dimensions and shapes undera common set of focus and exposure conditions. However, due to thecomplex non-linear nature of the pattern transfer process, OPC remains adifficult technique to implement, test, and insert into production withthe assurance that all features of interest are correctly transferredfrom mask to wafer in a manufacturing fab. It is particularly difficultto determine which limited set of structures should be measured andtracked on a regular basis to guarantee that all of the structures inthe circuit design are printed correctly.

The definition of common process window used to this point has beenphrased in terms of CD performance as the sole metric of processcapability. In fact, the common process window for a viable process mustbe such that the entire three-dimensional pattern is replicatedfaithfully, including the sidewall profile and height of the remainingresist pattern after development. A complete, common process windowwould insure that the CD, sidewall angle (SWA), and resist loss (RL)during development are all within specifications for all structures inthe circuit layer being patterned. In practice, the process window isoften defined only in terms of the CD. A typical process window may bedefined as the region of the F-E plane over which the criticaldimensions are patterned within specified tolerances, such as +1-10% ofthe nominal target dimension.

In addition to focus and exposure, many other parameters can have anadverse impact on the common process window, including, but not limitedto, lens aberrations (Zernike aberrations, scattered light, and othermid- to long-range spatial frequency errors), imperfections in theexposure tool illumination system (uniformity, localized partialcoherence, and localized variations in pupil filling), and less thanoptimal optical proximity corrections. (See Wong, “ResolutionEnhancement Techniques in Optical Lithography,” SPIE Press, Bellingham,Wash., Volume TT47, ISBN 0-8194-3995-9 (2001), and Mahajan, “AberrationTheory Made Simple,” SPIE Press, Bellingham, Wash., Volume TT06, ISBN0-8194-0536-1 (1991)).

A typical integrated circuit device contains between 100 million toseveral billion structures, and there are typically several hundreddevices or chips on a typical silicon substrate with a diameter of 200or 300 mm. It is clearly impossible to measure the dimensions of everystructure on a single device, and even more so to measure every deviceon every production wafer. In practice, a limited set of structures isdefined, either within the device areas or placed in the scribe linesbetween device areas, for in-process monitoring and control.

One of the most difficult challenges in implementing complex OPC intoproduction, such as leading edge model-based OPC, is the difficulty indetermining which limited set of structures should be measured andtracked on a regular basis to guarantee that all of the structures inthe circuit design are printed correctly. Since all patterns responddifferently to changes in focus, exposure, and other process variables,a test structure that is sensitive to one parameter that must bemonitored and controlled may be insensitive to other criticalparameters. Thus, even if the process monitors seems to indicate thatthe process is in control, other patterns which are critical to theproper operation of the device may drift without being detected by themetrology plan of record. Insuring that all of the critical parametersare adequately monitored and controlled is a growing challenge inintegrated circuit manufacturing.

In today's wafer fabs, exposure tool related optical conditions such asfocus, exposure, illumination and aberration may be monitored using testmasks and test wafers using specially designed test patterns, or by anexposure tool's self-metrology while undergoing maintenance checks. Theoptical analysis in these approaches is not in-situ, i.e., the extractedoptical conditions are not present when product wafers are printed.Therefore the use of these data is limited.

It is also possible to use test patterns in the product mask's scribelines, i.e., the spaces between the device areas, to print testpatterns, and analyze the test patterns in the scribe lines to extractthe optical conditions when the wafer is printed. This approach willtake up the valuable space in the scribe lines, where many otherpatterns are needed, for example alignment patterns. Furthermore, thisapproach cannot analyze the optical conditions inside the device designitself.

In practice, a limited set of test structures is defined, either withinthe device areas or placed in the scribe lines between the device areas,for in-process metrology and process control. The dimensions of thesetest structures are usually chosen to be at or near the minimum featuresize that will be patterned within the device, and are referred to as“the CD” of the layer being patterned regardless of whether the width ofthe structures being measured is actually the minimum or most criticaldimension in the device.

The CD is intended to be measured as near as possible to the base of thethree-dimensional resist structure, that is, at the resist-substrateinterface, to assure the optimum feature size for subsequent processsteps such as etch, film deposition, polishing, and ion implantation.This is often difficult due to the top-down viewing angle of scanningelectron microscopes used for CD metrology (CD-SEMs) and the high aspectratio of the three-dimensional profiles, which typically approach 3:1 (3units depth in the z-direction for every one unit of width in the x-yplane). Scatterometry (SCD) and tilt-SEMs are intended to help resolvethis problem and may be used to determine metrics of resist profile andresist loss in addition to CD, but they suffer from other sources oferror and uncertainty as well. While it is possible to obtain areasonable approximation of the CD at the resist-substrate interface forthe specified test structures when the process is very close to optimal,the uncertainty in the true CD increases as the process drifts away fromthe optimal settings.

CD data from multiple test structures with different geometries may becollected in an effort to more closely monitor the common process windowof the entire pattern transfer process. While this comes closer tomonitoring the full process than simply measuring a single type of teststructure, it falls far short of the ultimate goal of representing theperformance of all patterns and structures within the device, includingthe effects of the local environment on the dimensional stability ofeach pattern. In addition, a full characterization of the common processwindow would need to take into account the full three-dimensional shapeof the metrology patterns, including sidewall angle, sidewall curvature,or other deviations from an idealized trapezoidal shape, and the heightof the pattern indicating resist loss from the top of thethree-dimensional structure.

The non-linear manner in which each different structure responds to itsown local environment and to the global changes in optical and otherprocess conditions throughout the pattern transfer process from mask towafer creates a complex manufacturing environment where it is difficultto determine, from a limited number of measurements, whether or not allof the desired features within the device are printed withinspecification, and whether or not all of the variable process conditionparameters are being maintained at or near their optimum operatingpoints. This leads to several related problems:

(1) The limited amount of metrology data and test structures cannotadequately ensure that the CDs across the device are withinspecifications and that the entire device will operate as intended.Since typical manufacturing cycle times can take several months, it isextremely disruptive to the entire business when a large number ofwafers turn out to have unacceptably low yield of devices that workproperly, requiring new lots to be started and deliveries to besubstantially more expensive and behind schedule.

(2) Even when a variation in CD performance is detected, the propercorrective action to take is often unclear due to the confoundinginfluences of focus, exposure, and other process conditions.Historically the easiest adjustment to make has been the exposure dose(See Levinson, “Lithography Process Control,” SPIE press, Bellingham,Wash., Vol. TT28, ISBN 0-8194-3052-8 (1999)). However, this blindcorrection, that is, using only a single adjustable parameter to force adesired output on a limited set of data regardless of which of manyparameters actually caused the process to deviate in the first place,often means that the root cause of the CD variation remains undetectedand incorrectly compensated.

(3) If the process variation of the test structures was caused by adrift or shift in other parameters besides exposure, a simple exposurecompensation or other blind adjustment may be adequate to correct the CDof the test structures, but not of all structures within the device,which could result in the final devices having reduced functionality andyield.

(4) Although crude correction such as a blind exposure dose adjustmentmay bring the metrology target CDs back into specification, if the errorwas in fact caused by a focus drift or other optical error instead of anexposure drift, the resulting resist profiles will not have the correctsidewalls intended for the process. Thus, when the wafers are etched,the final profile of the resulting etched structure may be incorrect,leading to improper circuit operation and device failure.

(5) Even if the wafers in question are adequately corrected by a simpledose or other blind correction, other wafers that are to be patternedwith other device layouts may be patterned incorrectly with the samerelative correction. Thus, by changing the only one parameter tocompensate for drifts in other parameters, the process controlalgorithms for the entire production line may become unstable.

(6) Even if the process can be adequately corrected with a globaladjustment of the exposure dose or some other blind correction, if theunderlying source of the process variation is not correctly identifiedand corrected, it may continue to drift further until no amount ofcompensation of a single parameter can recover the desired processoutputs. At that point, the orderly flow of wafers through the entireprocess line may be disrupted as CDs begin to fall outside thespecification limits and the root cause analysis of the true source ofthe variation must be initiated at great cost and disruption to themanufacturing environment.

Many different approaches have been developed to try to improve CDmonitoring and control systems. In particular, a number of techniqueshave been disclosed to try to separate out the influences of focus andexposure to solve some of the problems listed above. While some of thesemethods are partially successful, they all suffer from drawbacks interms of metrology calibration and/or requirements for extensive teststructures that do not adequately represent the circuit features. Inaddition, they are limited to focus and exposure only and do not includeother optical parameters of interest. Many of these techniques requireextensive metrology and test structure calibration and cannot be easilyadapted to changes in circuit design and/or process targets.

U.S. Pat. No. 6,414,326 to Nguyen discloses a simple method todeconvolve focus and exposure errors by measuring two different teststructures, one with an isolated pitch and the other with a dense pitch.Since these pitches respond differently to focus and exposure, a givencombination of CD errors for the respective patterns should correspondto a unique set of focus and exposure errors. Like many similar efforts,this approach still suffers from an ambiguity in the sign of the focuserror: a given combination of isolated and dense CD errors could be dueto a focus shift of a given magnitude but in either the positive ornegative direction. Nguyen teaches a possible solution using astigmatismof the lens to differentiate between the two focus directions forvertically vs. horizontally oriented features, but this requiresextensive characterization of lens astigmatism across the field whichmay also be convolved with other lens parameters.

U.S. Pat. No. 6,673,638 to Bendik et al. discloses a system for creatingline end test structures that are modified to print in a deliberatelydefocused state even when the exposure tool is at best focus. Acomparison between differently modified structures can uniquelydetermine dose and focus errors; the sign ambiguity of the focus can besolved by printing features of opposite polarity (lines and spaces).This approach is restricted to special targets in the scribe line, notactual device geometries, and depends on line end metrology, which isless precise and more susceptible to convolution with other opticalaberrations than line width (CD) measurements.

U.S. Pat. No. 6,929,892 to Shishido et al. discloses the use of an SEMwith tilt capability or scatterometry to determine the sidewall profileof a highly isolated feature, which is known to be more susceptible tofocus-induced variations than dense structures. The ambiguity in thesign of the focus error is said to be resolved by measuring isolatedstructures of opposite polarity. Like U.S. Pat. No. 6,673,638, thisapproach is limited to scribe line test structures, not actual devicegeometries, and the focus sensitive isolated structures are alsosensitive to other aberrations.

U.S. Pat. No. 6,803,995 to Ausschnitt discloses a focus control systemusing special scribe line patterns wherein line end shortening of thesegments of a long pattern (“schnitzl”) can be imaged as line widthchanges in an optical measuring system. The effects of focus andexposure can be deconvolved using targets of opposite polarity, but thesign ambiguity of the best focus position can only be resolved byexposing some fields on the wafer at deliberately defocused conditions,resulting in potential lost chips and complicated exposure and metrologyschemes. U.S. Pat. No. 6,643,596 to Firth et al. also discloses a systemof deliberately inducing focus perturbations on a lot-by-lot basis toupdate a focus model. This results in better focus metrology at theexpense of negatively impacting the CD control of each lot processed atless than optimal focus.

Various applications of scatterometry have been described as alternativeapproaches to separating the effects of focus and exposure while alsoproviding CD metrology data. U.S. Patent Application Publication No.2004/0190008 to Mieher et al. discloses the use of CD, SWA, and RLvalues derived from SCD spectra to determine a unique combination offocus and exposure conditions. U.S. Pat. No. 6,429,930 to Littau et al.discloses the use of SCD spectra to uniquely determine focus andexposure by comparison to a library of stored spectra and U.S. PatentApplication Publication No. 2004/0223137 to Littau et al. disclosesextracting an SCD-derived cross section and comparing it to simulated ormeasured cross sections as a function of F-E conditions. All SCDapproaches are limited to arrayed test structures placed in the scribelines, not device geometries, and are typically limited toone-dimensional line space patterns or at best regular arrays oftwo-dimensional features such as contact holes. It should also be keptin mind that while SCD does provide highly repeatable measurements, theCD values extracted from SCD curves are all modeled results based onregression, simulation, or lookup tables and do not represent directmeasurements of the actual CD or sidewall angle.

Several approaches have been described using signal analysis of the SEMprofiles or images to determine the sidewall profile by comparison tostored reference signals. U.S. Patent Application Publication No.2002/0051567 to Ganz et al. discloses collecting images of assortedstructures and comparing them to a library of the same patterns printedat different focus and exposure settings. This technique does notdescribe how an adequate set of patterns is determined to insurepredictability over a range of F-E conditions, or to separate F-Eeffects from other imaging errors, nor does it result in CD metrologydata of sufficient quantitative precision to insure that the process isoperating within specifications. U.S. Pat. No. 6,909,930 to Shishido etal. discloses collecting CD-SEM data and analyzing the shape of the SEMline scan profiles or images to determine whether the CD errors are dueto focus or exposure. This approach requires extensive calibration,therefore it can only be applied to specific structures which arecarefully characterized beforehand and stored in a library. Thistechnique is also sensitive to slight changes in the SEM line scanprofile which may be induced by changes in the SEM conditions ratherthan the exposure tool conditions. U.S. Pat. No. 6,913,861 to Shishidoet al. discloses creating special scribe line test structures such asdiamond or other highly tapered patterns where the exposure dose iseffectively varied along the structure. SEM line scan analysis isperformed on these special structures to deconvolve the effects of focusand exposure. U.S. Pat. No. 6,791,082 to Komuro et al. discloses asimilar approach using scribe line patterns of opposite polarity toseparate the impact of focus and exposure and to resolve the ambiguityin the sign of any focal plane error. Both of these approaches depend onspecial scribe line targets, not actual device geometries, and rely onextensive look up tables and the stability of the SEM line scan toextract the condition of the exposure tool.

All of the above approaches aim to separate the contributions of focusand exposure on pattern CDs under the tacit assumption that all otheroptical parameters can be effectively ignored. U.S. Pat. No. 6,795,163to Finders et al. shows that these other parameters can be as important,if not more so, in determining the patterned CD errors of a given linewidth as a function of pitch. Finders et al. disclose that the partialcoherence, sigma, of the exposure tool can be adjusted to tilt the CDvs. pitch curve to suffer less CD variation across a range of pitches.This illustrates that even in a system that can adequately separatefocus and exposure contributions under ideal conditions, the addition ofa small illumination error can drastically impact the measured CDperformance and so lead to incorrect determination of the computed focusand exposure adjustments.

It is therefore highly desirable to have a system and technique toeffectively monitor or observe the lithography process's F-E conditioninside the device with actual circuit design patterns, on any selectedwafers and at any selected dice on that wafer. This need can be realizedby a system and technique that can extract the F-E conditions of thelithographic process directly from printed patterns on product wafers,on any selected wafers and at any selected dice on that wafer.

It is further desired that this system and technique be able to insurethe correct patterning of all structures within the device, not just thestructure being measured. This need can be realized by a system andtechnique that can optimize the selection of the optimum set of measuredpatterns from actual device design patterns that forms a completecharacteristic set such that the correct patterning of this set ofpatterns serves as measurable proof that all patterns within the deviceare reproduced within specifications.

Beyond the traditional F-E process window, other optical conditions,e.g., NA, illumination conditions, and aberrations, will also drift overtime and impact the patterning of the device structures. It is alsohighly desirable that these other drifts can be extracted directly fromthe observations of the printed circuit patterns on wafers.

It is further desirable that this system and technique should be capableof quickly and effectively identifying specific patterns within thedevice that, when measured, will provide precise and specific feedbackto the lithography control system indicating not just the CD values, butalso the specific corrections that need to be applied to the system tobring the CDs back within specifications and to maintain the exposuretool and the lithography process at their optimum operating conditions.These needs can be realized by the system and technique to locate acomplete characteristic set of patterns within the circuit whosecollective response to the multivariate optical and process variationsprovides a unique monitoring and control solution.

SUMMARY

A method for identifying process window signature patterns in a devicearea of a mask is disclosed. The signature patterns collectively providea unique response to changes in a set of process condition parameters tothe lithography process. The signature patterns enable monitoring ofassociated process condition parameters for signs of process drift,analyzing of the process condition parameters to determine which arelimiting and affecting the chip yields, analyzing the changes in theprocess condition parameters to determine the corrections that should befed back into the lithography process or forwarded to an etch process,identifying specific masks that do not transfer the intended pattern towafers as intended, and identifying groups of masks that share commoncharacteristics and behave in a similar manner with respect to changesin process condition parameters when transferring the pattern to thewafer.

In one embodiment, a method for identifying process window signaturepatterns comprises performing simulations of a lithography process,using a representation of a device area of a mask, at two or morenon-nominal locations and at nominal condition in a process window toproduce simulation results, determining a simulated value for eachmetric in the pattern-metric pairs from the simulation results,determining simulated values from the simulation results for at leastone metric at the two or more non-nominal locations and at nominalcondition in the process window, wherein the simulated values for the atleast one metric are determined at a plurality of locations with thedevice area. The method further comprises, for each of the plurality oflocations within the device area determining a difference betweensimulated values for the at least one metric at the two or morenon-nominal locations in the process window and the simulated value forthe at least one metric at nominal condition, identifying a number oflocations within the device area where the difference for the at leastone metric is above a threshold, if the number of identified locationsis greater than a predetermined number, modifying the threshold andidentifying the locations within the device area where the differencefor the at least one metric is above the threshold until the number ofidentified locations within the device area is less than thepredetermined number, and assigning patterns associated with the numberof identified locations within the device area that is less than thepredetermined number to be signature patterns.

In one embodiment, a method for using the signature patterns forlithography process control comprises receiving measured values of theat least one metric associated with the signature patterns, the measuredvalues having been obtained from measurements of the signature patternson a wafer processed by the lithography process using the mask, findinga location in the process window where a total difference measurebetween the measured values of the at least one metric associated withall of the signature patterns and the simulated values of the at leastone metric associated with all of the signature patterns at the locationin the process window is minimized, and determining a change between thelocation in the process window where the total difference measure isminimized and nominal condition to determine a change in the lithographyprocess. The change in the lithography process is then fed back to thelithography process for process control or fed forward to anotherprocess, for example an etching process.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one embodiment for an exposure tool;

FIG. 2 is a diagram of one embodiment of a process window defined in afocus-exposure space;

FIG. 3 is a diagram of multiple overlapping process windows in afocus-exposure space;

FIG. 4 is a flowchart of method steps for identifying process windowsignature patterns of a mask, according to one embodiment of theinvention;

FIG. 5 is a flowchart of method steps for using the identified processwindow signature patterns for process control of a lithography process,according to one embodiment of the invention;

FIG. 6 is a flowchart of method steps for identifying process windowsignature patterns of a lithography process, according to anotherembodiment of the invention;

FIG. 7 is a chart showing sensitivities of pattern-metric pairs tovarious process condition parameters, according to one embodiment of theinvention;

FIG. 8 is a chart showing sensitivities of pattern-metric pairs tovarious process condition parameters, according to another embodiment ofthe invention;

FIG. 9 is a flowchart of method steps for identifying changes in thevalues of process condition parameters based on changes in the metricsof process window signature patterns, according to one embodiment of theinvention;

FIG. 10 is a flowchart of method steps for iteratively refining asignature pattern matrix and solving for changes in the values ofprocess condition parameters, according to one embodiment of theinvention;

FIG. 11 is a chart showing critical dimensions of three products vs.number of lots processed;

FIG. 12 is a chart showing values of process condition parameters vs.number of lots processed; and

FIG. 13 is a diagram of one embodiment of a lithography simulationsystem, according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

The method of the invention enables direct process window extractionfrom device patterns on production wafers by determining a set ofprocess window signature patterns (also referred to herein as “signaturepatterns”) within a device design that, when monitored as part of alithography process control system, provide clear feedback to thecontrol system on how the lithography process is performing, whether theCDs for all features within the device, not just the measured features,are within specification, and which process condition parameters shouldbe adjusted and by how much in order to maintain the lithography processand exposure tool at the optimum operating condition.

It is well known that device patterns, depending on their environment,respond differently to changes in focus, exposure dose, and otherprocess condition parameters. The collective response of the signaturepatterns to the entire range of process condition parameters can bedecomposed and analyzed to determine the exact process conditionparameters that have drifted and by exactly how much. The signaturepatterns can be selected for any given device layout. The signaturepatterns are the minimum subset of patterns in the design area of a maskthat allow for the required level of process control with the minimumamount of metrology effort and expense. The metrology data from thesignature patterns is used to extract the conditions of all processcondition parameters that caused drift in the imaging process. Themethod of the invention includes simulating the impact of these processdrifts on every signature pattern within the circuit across the fullfield of the exposure tool and determining what corrections should beapplied to the lithography process to optimize the pattern transfer forthe specific devices being patterned, as well as what corrections toprocess condition into the fab-wide process control system to be appliedat later steps in the process and/or to other products which would besubsequently patterned using the same process equipment.

As used herein, the term “process condition parameters” refers to any ofthe exposure tool parameters and/or the lithographic process parametersthat can be modeled and whose impact on the pattern transfer process canbe simulated, whether or not that parameter can be adjusted andcorrected. The process condition parameters may also be called “inputparameters” to the lithography process. The process condition parameterscan be subdivided into adjustable and non-adjustable parameters, and theadjustable parameters may be further sub-divided into those that areadjustable by the end user and those that require more extensive serviceprocedures to adjust. The user response to changes in the processcondition parameters may differ depending on whether or not theparticular parameter is user-adjustable. These distinctions will impactthe user response to observed changes in the process conditionparameters, but it does not restrict which process condition parametersmay be monitored.

While no useful set of patterns can be found within a device design suchthat each pattern would be sensitive to the variation of one and onlyone process condition parameter, there does exist a set of signaturepatterns such that the combined response of the signature patternsuniquely identifies the combination of all process condition parametervariations that contributed to the printing of the circuit design. Thatis, while no specific individual pattern can be used in isolation todetect variations in focus and only variations in focus while anotherpattern monitors exposure, another monitors spherical aberration,another monitors partial coherence of the illumination system, and soon, there does exist a set of signature patterns whose response can beuniquely decomposed to identify any deviations from nominal in any ofthe above-mentioned process condition parameters and to determine thespecific corrections to apply to each of the process conditionparameters. Even though no single signature pattern acts as a uniqueprobe of one and only one process condition parameter, the set ofsignature patterns acts collectively as if it were a complete set ofprobes, each of which responds to one and only one of the processcondition parameters of interest.

The full chip simulation system disclosed in U.S. Pat. No. 7,003,758,the subject matter of which is hereby incorporated by reference in itsentirety, can be used to automatically simulate the response of a fullchip design to each of the process condition parameters of interest. Thesimulation system may also be used to determine the optimum set ofsignature patterns that have the highest sensitivity to the processcondition parameters of interest with the minimum number of measurementsrequired, create a metrology recipe to collect the required imagesand/or CD data from printed wafers, extract metrics of interest (imagecontours, CD values, or other metrics of pattern quality) from themeasured data, and analyze these metrics to uniquely identify allvariations in the process condition parameters. The extracted processcondition parameters can be fed back into the simulation system topredict the quality of the printed patterns across the chip and acrossthe full wafer, determine if the quality is acceptable or not, and passback a go/no go decision flag and correction parameters to themanufacturing control system.

In a simple example for positive photoresist, as the exposure dose isreduced, the CD of all line patterns (chrome patterns on the mask) willincrease and the CD of all space patterns will decrease. However, whenfocus changes, some spaces and line patterns will grow and others willshrink, depending on the exact pattern environment, such as the pitchand OPC correction patterns in the vicinity, and the exact exposuredose. In this example, a full chip simulation is performed to find thoseisolated lines, and line-space pairs with the right pitch, that behavedifferently due to focus and exposure changes. Using the measuredrelative change in linewidth for these patterns, the location in thefocus-exposure process window can be identified. This example isprovided only as the simplest representation of the more general case.

FIG. 4 is a flowchart of method steps for identifying process windowsignature patterns in a circuit design, according to one embodiment ofthe invention. In the disclosed example, the method of FIG. 4 willidentify 20-30 signature patterns, which is typically a sufficientnumber of signature patterns to accurately monitor a lithographyprocess. However, a larger number of signature patterns may beidentified for use with a fast measurement metrology system. In optionalstep 412, locations throughout the entire device area of a mask where CDmeasurements are to be taken are selected. The device area of a maskincludes the patterns that will be printed on a portion of a wafer thatwill ultimately become the finished integrated circuit device. Thedevice area of the mask does not include any test patterns that will beprinted in the scribe lines of a wafer. For ease of illustration, only aCD measurement metric is discussed in conjunction with FIG. 4. However,any other suitable metric or combination of metrics is within the scopeof the invention. For example, the metrics can be one-dimensionalmetrics such as CD measurements, two-dimensional metrics such as cornerrounding, line-end pullback, narrowing, widening, and corner-to-cornerseparation, three-dimensional metrics such as resist thinning, sidewallangle, top profile rounding, footing at the resist-substrate interface,sidewall curvature, and asymmetry between sidewalls, or any combinationof these. All of these metrics can be measured on printed wafers byexisting metrology tools, such as CD-SEMs, tilt SEMs, scatterometers,optical CD metrology tools, and atomic force microscopes (AFM). Thereare numerous references in the literature to feature extraction, such asAllgair et al, “Characterization of OPC Features,” SPIE Proc., Vol.4344, pp. 200-207 (2001), Wiaux et al., “Assessment of OPC EffectivenessUsing Two-dimensional Metrics,” SPIE Proc., Vol. 4691, pp. 395-406(2002), and references cited therein.

In step 414, locations in a process window are selected includingnominal condition. The locations in the process window are defined byvalues of process condition parameters to the lithography process. Theprocess condition parameters may include focus, exposure dose, filmstacks, lens aberrations, illumination shifts, coherence, and/or anyother process condition parameters to the lithography process, whetheruser-correctable or not. In the following discussion, the process windowis in a two-dimensional focus-exposure space for ease of illustration.The locations in the process window are selected so as to includemultiple values of each process condition parameter, i.e., varyingvalues of focus and exposure dose, and the nominal condition of eachprocess condition parameter. In a preferred embodiment, the selectedlocations in the process window include two or more non-nominalconditions and nominal condition. The distance between the varyingvalues of the process condition parameters is preferably the minimumadjustable step size of the process condition parameter.

In step 416, the lithography process is simulated using datarepresentative of the device area of a mask at each of the selectedlocations in the process window, producing simulation results for eachvalue of the process condition parameters. The lithography process issimulated using a lithography simulation system, one embodiment of whichis described below in conjunction with FIG. 13. The simulated resultsmay be simple aerial images with a fixed or variable threshold imposedto simulate the edges of a resist pattern, may include more complexresist models, including first-principles models of the resist responseto exposure and post-exposure baking, acid and base diffusion effects,and other kinetic effects associated with chemically amplified resistsystems, or may include the aerial image simulation followed by asimplified resist model, such as a lumped parameter model or othersimplified physical representation of the full resist process. Suchsimplified models are well known (see Brunner, “Impact of Resist Blur onMEF, OPC and CD Control,” SPIE Proc., Vol. 5377, pp. 141-149 (2004)) andare often preferred since they are much more computationally efficientto implement than first-principles models, and have been demonstrated toproduce simulated resist patterns with extremely good correlation toactual measured resist patterns.

In step 418, the CDs of the patterns in the simulation results aremeasured to produce simulated CD values. The CDs are measured at thelocations selected in optional step 412 or at other regularly-spacedlocations within the device area. Then, in step 420, for each CDmeasurement location, the distance between the simulated CD values atthe selected locations in the process window and the simulated CD valueat nominal condition is determined. In one embodiment, the minimumdistance can be determined in a root-mean-square sense. For example,

${D_{nom}(j)} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}{W_{i}\lbrack {{{CD}( {E_{i},F_{i}} )} - {{CD}( {E_{0},F_{0}} )}} \rbrack}^{2}}}$where j=(1, 2, . . . , M), M is the total number of the selectedlocations throughout the entire device area of a mask where CDmeasurements are to be taken, D_(nom)(j) is the difference between thesimulated CD values at the selected locations in the process window andthe simulated CD value at nominal condition for the j-th CD measurementlocation, N is the number of selected locations in the process window,W_(i), is a weighting factor, CD(E_(i),F_(i)) is the simulated CD valueat location (E_(i),F_(i)) in the process window, and CD(E_(o),F_(o)) isthe simulated CD value at nominal condition. In step 422, the CDmeasurement locations for which the distance D_(nom)(j) is greater thana threshold are identified. The value of the threshold is preferablybased on the noise of the lithography simulation system used to producethe simulated results.

If, in step 424, it is determined that the number of CD measurementlocations for which the distance D_(nom)(j) is greater than thethreshold is greater than 30, then in step 426 the threshold isincreased and the method returns to step 422. If, in step 424, it isdetermined that the number of CD measurement locations for which thedistance D_(nom) is greater than the threshold is less than 30, then themethod continues in step 428. In step 428, the patterns associated withthe or fewer CD measurement locations are assigned to be the processwindow signature patterns. In another embodiment, if the number of CDmeasurement locations for which D_(nom)(j) is greater than the thresholdis much fewer than 30, then the threshold will be decreased until asufficient number of locations are identified.

FIG. 5 is a flowchart of method steps for identifying changes in alithography process, according to one embodiment of the invention. Instep 512, a metrology recipe for measuring patterns on processed wafersis created based on the signature patterns and their associated metrics.For example, if focus error and its variations across the exposure fieldis a process condition parameter to be evaluated, it is useful tomeasure the signature patterns near the four corners of the exposurefield of the exposure tool. If stray light is a process conditionparameter to be evaluated, signature patterns can be measured in regionsof the device area where stray light is expected, typically where thechrome pattern is sparse. The metrology recipe can be created eithermanually or automatically. Automatic metrology recipe creation mayinclude insertion of wafer alignment, pattern recognition targets, andsimulations of what the targets will look like in the metrology tool tofacilitate automatic alignment and pattern capture. To save measurementtime and effort, the pattern recognition targets may be defined as thesignature patterns themselves, or easily identifiable structures closeenough to the signature patterns to allow rapid measurement after thepattern recognition is complete with little or no stage movementrequired between pattern recognition and metrology data collection. Inaddition to the locations of the signature patterns, the metrologyrecipe may include the metric of interest to be measured for eachpattern. The metrology recipe, including all necessary alignment andpattern recognition images, target locations, and metrology metrics ofinterest, may be stored in a format that can be read by the metrologytool with no operator intervention required.

Next, in step 514, wafers are processed using the lithography processand the mask to produce printed patterns on the wafer. In step 516, themetrics of the printed signature patterns on the wafer are measuredaccording to the metrology recipe. In step 518, the location in theprocess window where a total difference measure between the simulatedvalue for each metric and the measured value for each metric isminimized for all signature patterns is identified. In one embodiment,the total difference measure between the simulated and measured valuefor each metric is calculated as follows:

${G( {F,E} )} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}{W_{i}\lbrack {M_{wafer}^{(i)} - {M_{sim}^{(i)}( {F,E} )}} \rbrack}^{2}}}$where G(F,E) is the difference measure as a function of the location inthe process window (here values of focus, F, and exposure dose, E), N isthe number of the signature patterns, where there is one metric measuredfor each signature pattern, W_(i) is a weighting factor, M_(wafer)^((i)) is a measured value of metric M of the i-th signature pattern,and M_(sim) ^((i))(F,E) is a simulated value of metric M of the i-thsignature pattern at a location (F,E) in the process window. Thelocation in the process window, i.e., the values of focus and exposuredose (F,E), that results in the minimum value for G(F,E) is identified.In one example, determining the minimum value of G(F,E) may be performedby selecting an arbitrary location in the process window, solving forG(F,E) for that location, and iterating these steps until the minimumvalue of G(F,E) is determined. Any appropriate techniques fordetermining the minimum value of G(F,E) are within the scope of theinvention, for example using a steepest decent algorithm or using alook-up table.

In step 520, the distance between the identified location in the processwindow and nominal condition of the lithography process is determined.This distance identifies a change in the process condition parameters(in this example, focus and exposure dose) of the lithography process.This change in the process condition parameters can then be fed back toa control system for the lithography process or fed forward to a controlsystem for other processes, for example an etch process.

FIG. 6 is a flowchart of method steps for identifying process windowsignature patterns in a device area of a mask, according to anotherembodiment of the invention. In step 612, pattern-metric pairs areselected for the mask being evaluated. For each pattern or some selectedsubset of patterns in the device area of the mask, one or more metricsare selected to form pattern-metric pairs. That is, each selectedpattern may be uniquely paired with a metric or each selected patternmay be paired with more than one metric. The metrics may be anyone-dimensional metric, two-dimensional metric, or three-dimensionalmetric capable of being measured on printed wafers by existing metrologytools. In step 614, process condition parameters of the lithographyprocess are selected and a range of values for each of the selectedprocess condition parameters is selected. The process conditionparameters may include focus, exposure dose, film stacks, lensaberrations, coherence, illumination shifts, and/or any other processcondition parameters to the lithography process, whetheruser-correctable or not. In step 616, the lithography process issimulated using data representative of the device area of the mask overthe range of values of the selected process condition parameters toproduce simulated results. For greatest accuracy, a set of simulationsis performed for each of the process condition parameters, where eachset of simulations is performed with varied values of the currentprocess condition parameter while all other process condition parametersare kept fixed. Fewer numbers of simulations may be adequate for thepurpose of identifying signature patterns.

In step 618, for each pattern-metric pair, differences between themetrics of the simulation results are determined and the sensitivity ofeach metric to each of the process condition parameters is determined.The sensitivity can be expressed as an average rate of change in ametric across the full range of values for the process conditionparameters, as a derivative (instantaneous rate of change at specificvalues of the process condition parameters), as a total range ofdeviation, or some similar, normalized indication of relativesensitivity with respect to a specific process condition parameter ofinterest.

Next, in step 620, a matrix of the sensitivities of the metrics to theprocess condition parameters is created. In one embodiment, the matrixhas one column for each process condition parameter and one row for eachpattern-metric pair. This matrix is referred to herein as the patternmatrix.

FIG. 7 shows an ideal case of a pattern matrix, in which certain rows inthe matrix have zeros in every column except one, and each columnintersects at least one such row. This matrix represents the idealsituation where, for each process condition parameter of interest, thereexists a specific pattern-metric pair that responds to that particularprocess condition parameter, and only to that particular processcondition parameter, and for each process condition parameter thereexists at least one pattern-metric pair that responds exclusively tothat process condition parameter. Each of the patterns in thesepattern-metric pairs would be a perfect signature pattern, and the setof perfect signature patterns would allow for perfect monitoring of thelithography process such that if any process condition parameter isvaried from its nominal position, the changes observed in the metricsassociated with the signature patterns would enable a control system tounambiguously identify which process condition parameter had varied andby exactly how much. As noted above, this idealized case is not possiblegiven the complex responses of most patterns to changes in processcondition parameters. Since this idealized pattern matrix resembles amatrix of eigenvalues, the patterns of the pattern-metric pairs may alsobe called “eigen patterns.”

In a slightly more realistic but still idealized example, FIG. 8 shows apattern matrix where some of the pattern-metric pairs respond to morethan one process condition parameter, and one process conditionparameter affects more than one pattern-metric pair. While a uniqueone-to-one correspondence between a pattern-metric pair and an processcondition parameter does not exist, the matrix of FIG. 8 is still highlydesirable for process control since the sensitivities are negativelycorrelated, i.e., the sensitivity of one pattern-metric pair to twoprocess condition parameters and the response of multiple pattern-metricpairs to a single process condition parameter are in oppositedirections, enabling the root cause of any process drift to be easilydiagnosed from the measured sensitivity data. The simultaneous existenceof both positive and negative correlation, by two differentpattern-metric pairs, enables the identification of a unique solution tothe set of process condition parameters. In a more general sense, thenumber of linearly independent or nearly independent pattern-metricpairs needs to be equal to or greater than the number of processcondition parameters. Stated another way, the pattern matrix should beas non-singular as possible, i.e., a minimum set of pattern-metric pairsis needed such that the rank of the pattern matrix is the same as thenumber of process condition parameters so that changes to all of theprocess condition parameters can be solved for robustly.

In step 622, a subset of the pattern-metric pairs that have the mostlinearly separable sensitivities to changes in the values of the processcondition parameters is selected. In one embodiment, the pattern matrixis analyzed to find the pattern-metric pairs where a limited number ofprocess condition parameters have the most dominant effect on the metricrelative to all of the other process condition parameters. Thesepattern-metric pairs form an initial subset of pattern-metric pairs. Arequirement for the initial subset of pattern-metric pairs is that foreach process condition parameter, at least one pattern-metric pair withhigh sensitivity to that process condition parameter is included. Inanother embodiment, the pattern matrix is analyzed to determine theminimum number of pattern-metric pairs required to satisfactorilymonitor the lithography process. This determination can be based onsample testing of a submatrix of the sensitivities to determine a set ofpattern-metric pairs with the most easily deconvolved sensitivities,i.e., where the sensitivities of the pattern-metric pairs to a range ofdifferent process condition parameter variations can be used to mostaccurately solve for the correct values of variations of the processcondition parameters. This sample testing can be performed using thelithography simulation system described below in conjunction with FIG.13.

In another embodiment, the full pattern matrix is analyzed using othermeans, such as algebraic testing. In this case, the matrix can beanalyzed and the initial subset of pattern-metric pairs that have themost nearly orthogonal set of sensitivities, that is, the set where thesensitivities to the full set of process condition parameters are asdifferent as possible, are identified. Various statistical metrics canbe employed to identify the optimum subset of pattern-metric pairs andselect the minimum number of pattern-metric pairs needed for reliableprocess monitoring and control, including, but not limited to, metricsof the respective cross-correlation between the various pattern-metricpairs and process condition parameters and metrics of the orthogonalityof the sensitivities of the pattern-metric pairs.

In step 624, the initial subset of pattern-metric pairs is evaluated.The initial subset of pattern-metric pairs may be evaluated based on howdominant one or more process condition parameters is compared to theother process condition parameters, on the sensitivity of thepattern-metric pair to changes in the process condition parameters, andon other factors, such as ease of measurement of the specific metricusing the metrology tools available. A satisfactory subset ofpattern-metric pairs must have at least a number of pattern-metric pairsequal to the number of process condition parameters and must respondstrongly to multiple different process condition parameters, but withlinearly independent sensitivities.

If the initial set of pattern-metric pairs is not satisfactory forreliable monitoring of the lithography process, then in step 626 afurther subset of pattern-metric pairs is selected. Any of thetechniques described above for selecting the initial subset ofpattern-metric pairs may also be used to select the further subset ofpattern-metric pairs. Then the method returns to step 624 where thefurther subset of pattern-metric pairs is evaluated. Steps 626 and 624are repeated until a satisfactory subset of pattern-metric pairs isselected. In step 628, the patterns of the final subset ofpattern-metric pairs are assigned to be the signature patterns, and amatrix of the sensitivities of the signature patterns and theirassociated metrics to the process condition parameters is defined as thesignature pattern matrix (SP matrix).

In another embodiment, the pattern matrix has more than one column perprocess condition parameter. For example, if derivatives of thesensitivities are computed, the pattern matrix may include one columnfor each process condition parameter at each specific value of theprocess condition parameter to distinguish which pattern-metric pairsare most sensitive to that process condition parameter across the rangeof values assumed for that process condition parameter. The final subsetof pattern-metric pairs are then constructed to insure that changes inthe metrics of the final set of pattern-metric pairs can be wellresolved to determine the specific process condition parameter changes.This is especially useful if one of the patterns has a good response toa process condition parameter as its value changes in one direction, butnot in the other. In this case, it would be valuable to includecomplementary patterns, one of which responds clearly to changes ofeither sign in the process condition parameter, thereby insuringcomplete process coverage over the full range of process conditionparameters. Focus is a typical example of an process condition parameterwhere numerous patterns show a clearly one-sided response.

FIG. 9 is a flowchart of method steps for identifying changes in thevalues of process condition parameters based on measured changes in themetrics of signature patterns, according to one embodiment of theinvention. In step 912, a metrology recipe for measuring patterns onprocessed wafers is created based on the signature patterns and theirassociated metrics. For example, if focus error and its variation acrossthe exposure field is a process condition parameter to be evaluated, itis useful to measure the signature patterns near the four corners of theexposure field of the exposure tool. If stray light is a processcondition parameter to be evaluated, signature patterns can be measuredin regions of the mask where stray light is expected, typically wherethe chrome pattern is sparse. The metrology recipe can be created eithermanually or automatically. Automatic metrology recipe creation mayinclude insertion of wafer alignment; pattern recognition targets, andsimulations of what the targets will look like in the metrology tool tofacilitate automatic alignment and pattern capture. To save measurementtime and effort, the pattern recognition targets may be defined as thesignature patterns themselves, or easily identifiable structures closeenough to the signature patterns to allow rapid measurement after thepattern recognition is complete with little or no stage movementrequired between pattern recognition and metrology data collection. Inaddition to the locations of the signature patterns, the metrologyrecipe may include the metric of interest to be measured for eachpattern. The metrology recipe, including all necessary alignment andpattern recognition images, target locations, and metrology metrics ofinterest, may be stored in a format that can be read by the metrologytool with no operator intervention required.

In step 914, wafers are processed using the lithography process and themask to produce printed patterns on the wafer. In step 916, the metricsof the printed signature patterns on the wafer are measured according tothe metrology recipe. In step 918, differences between the measuredvalues of the metrics associated with the signature patterns and nominalvalues of the metrics are calculated to determine changes in themetrics. Optionally, previously-measured values of the metrics frompreviously-printed wafers may be used instead of the nominal values. Themeasured values of the metrics associated with the signature patternsmay also be used to determine the critical dimension distribution acrossthe exposure field of the exposure tool or across the entire wafer.

Then, in step 920, the equation A{right arrow over (M)}=SP*Δ{right arrowover (PC)} is solved to determine changes in the values of the processcondition parameters that resulted in changes to the metrics, whereΔ{right arrow over (M)} is a vector that includes the changes in thevalues of the metrics associated with the signature patterns, SP is theSP matrix, and Δ{right arrow over (PC)} is a vector that includes thechanges in the values of the process condition parameters. Since the SPmatrix is known, any changes in the values of the process conditionparameters can be determined for any measured changes in the metricsassociated with the signature patterns. There are many numerical methodsin the literature to solve a matrix inversion problem, and any suchnumerical method is within the scope of the invention. In step 922, thechanges in the values of the process condition parameters are providedto a lithography process control system. The FIG. 9 embodiment assumes aperfect linear system and only a single step matrix inversion operationsolves for the changes in the values of the process conditionparameters. For some SP matrices, where the values of the sensitivitiesin the SP matrix are not constant for all values of the processcondition parameters, a single step solution for the changes in thevalues of the process condition parameters may not be sufficientlyaccurate.

FIG. 10 is flowchart of method steps for iteratively refining asignature pattern matrix and solving for changes in the values ofprocess condition parameters, according to one embodiment of theinvention. FIG. 10 is another embodiment of steps 920 and 922 in FIG. 9,where the values of the sensitivities in the SP matrix are not constantfor all values of the process condition parameters. In may cases, thesensitivities that relate the process condition parameters to themeasured changes in the metrics depend upon the process conditionparameter settings, illustrated mathematically as Δ{right arrow over(M)}=SP({right arrow over (PC)})*Δ{right arrow over (PC)}, where Δ{rightarrow over (M)} is a vector that includes the changes in the values ofthe metrics associated with the signature patterns, SP({right arrow over(PC)}) is the SP matrix as a function of process condition parameters{right arrow over (PC)}, and Δ{right arrow over (PC)} is a vector thatincludes the changes in the values of the process condition parameters.In step 1012, the equation A{right arrow over (M)}=SP({right arrow over(PC₀)})*Δ{right arrow over (PC)} where {right arrow over (PC₀)} are thenominal values for the process condition parameters, is solved todetermine initial changes in the values of the process conditionparameters, Δ{right arrow over (PC₁)}, that resulted in changes to themetrics. In step 1014, the lithography process is simulated using thedata representative of the device area of the mask and the changed valueof the process condition parameters determined in step 1012 to producenew simulated values the metrics associated with the signature patterns,and the sensitivities in the SP matrix are recalculated. This matrixinversion can be solved in an iterative fashion using any of thewell-known numerical methods. Damping factors may be applied to thecomputed changes in the process condition parameters to preventoscillation about the solution point or other non-convergence issues.For example, if initial nominal process condition parameter values aredesignated as {right arrow over (PC₀)} and the first iteration ofsolving for the changes in the process condition parameters producesvalues of Δ{right arrow over (PC₁)} and new values of the processcondition parameters {right arrow over (PC₁)} such that Δ{right arrowover (PC₁)}={right arrow over (PC₁)}−{right arrow over (PC_(O))}, adamped set of values may be calculated using {right arrow over(PC_(1d))}={right arrow over (PC₀)}+d*Δ{right arrow over (PC₁)} whered<1. The damped values for the process condition parameters are thenused to determine new SP matrix coefficients SP({right arrow over(PC_(1d))}). In step 1016, differences between the measured values ofthe metrics associated with the signature patterns and the simulatedvalues of the metrics are calculated to produces measured changes in themetrics. In step 1018, new changes in the values of the processcondition parameters, Δ{right arrow over (PC₂)}, are determined usingthe changes in the metrics and the recalculated SP matrix. Δ{right arrowover (PC₂)} may be damped again and then added to an accumulated Δ{rightarrow over (PC)} to form a total Δ{right arrow over (PC)}. In step 1020,the changes in the metrics are compared to a threshold, and if thechanges are not below the threshold, the method returns to step 1014 andthe changes in the process condition parameters are iteratively solvedfor until final {right arrow over (PC)} values converge such that themeasured changes in the metrics are below the threshold. If, in step1020, the changes in the metrics determined in step 1016 are below thethreshold, then in step 1022 the changes in the values of the processcondition parameters are provided to the lithography process controlsystem.

A distribution of the changes in the process condition parameters acrossa wafer can be correlated with wafer chip yield maps. Statisticalanalysis (e.g., ANOVA, i.e., ANalysis Of VAriations) can be used toextract information on, for example, which process condition parameteris the critical yield limiting factor or which drift or variation incertain process condition parameters are most highly correlated withcertain signatures of yield fluctuation or excursions. These couldbecome critical information in fab yield management and yieldimprovements.

The calculated changes in the values of the process condition parametercan also be used to adaptively adjust the metrology recipe. As changesin the process condition parameters are detected, additionalmeasurements of the metrics associated with the signature patterns canbe incorporated into the metrology recipe, either to measure the metricsat more locations across the field and/or across the wafer, or tomeasure additional signature patterns for added sensitivity to theprocess condition parameters that are beginning to show unacceptabledrift. As additional data and/or corrective action are taken to improvethe process control of the lithography process, the metrology recipe maybe adaptively relaxed back to the minimum possible metrology burden.

The calculated changes in the values of the process condition parameterscan also be used to enable model-based advanced process control (MBAPC)of the lithography process. In conventional control schemes for a waferfab running multiple different products and processes, criticaldimension drift is usually monitored and corrected on aproduct-by-product and layer-by-layer basis. FIG. 11 illustrates anexample of CD measurements for three products plotted vs. number of lotsprocessed. The CDs for Product B in particular show a sudden changeafter lot 5. An exposure dose change for later lots of this productwould improve the CD measurements. However, it is unclear if a similarcorrection, or indeed, any correction at all, should be applied to theother two products. The situation becomes more complicated if theeffects of different exposure tools on the CD trends are included. Inthis case, even if the CD measurements for Product B exposed on exposuretool No. 1 begin to drift unacceptably so that an exposure dosecorrection is warranted for future runs on the same exposure tool, thecorrections that will be required for the same product on differentexposure tools is unknown. Finally, if multiple layers of multipleproducts on multiple exposure tools are tracked, even if the correctionto apply for a given layer of a given product on one exposure tool isknown, the proper correction for other layers of other products, or ofthe same layer of the same product on a different exposure tool, isunknown.

In the simplest scheme one can simply assume that all CD measurementdrifts are due to exposure dose variation and change the exposure dosefor all other layer/product combinations on exposure tool No. 1. Suchschemes have been implemented, but if the true source of the CDmeasurement drift was not an exposure dose change, but rather focus,aberration, or other process condition parameter, the net result may beincorrect compensation and a degradation, rather than an improvement, inthe CD performance of all product/layer combinations.

Using the signature patterns to determine changes in the processcondition parameters can improve the effectiveness of advanced processcontrol (APC) by correctly identifying the root cause of any observed CDmeasurement drift, thereby enabling the optimum exposure tool correctionnot just of the lot being measured, but of all subsequent lots of anyproduct/layer combination processed on the same tool, as well as allsubsequent lots of the same product/layer on other exposure tools. Asshown in FIG. 12, statistical process control to be implemented on theprocess condition parameters themselves, not just on the measuredmetrics, thereby imposing tighter control and enabling earlier detectionand correction of any drift in the process condition parameters. In FIG.12, the process condition parameter values calculated from all lots ofthree different products shows a strong linear drift in Parameter 3 thatneeds to be corrected for all products, as well as a weaker and noisierdrift in Parameter 2 that may require more measurement and possiblycorrection.

Using the signature patterns to determine changes in the processcondition parameters also allows for routine comparison between theprocess condition parameter changes on multiple exposure tools byidentifying which tools are most prone to drift and in need ofmaintenance, which tools are closest to their optimum operatingconditions and should be given priority for processing the most criticallots, which tools are most closely matched to one another to facilitateprocessing similar product layers on the most closely matched tools, andwhich tools are operating most closely to the conditions used inestablishing the OPC and RET models used to create the layouts for thedifferent products, thereby facilitating the orderly flow of productionlots to the tools that will pattern those lots as closely as possible tothe design intent.

Using the signature patterns to determine changes in the processcondition parameters is also useful in implementing an improved MBAPCcontrol system by removing or eliminating the effects of reticlemanufacturing variations from the lithography process control system. Ina conventional APC or statistical process control (SPC) system, it isoften observed that certain masks produce better CD distributions and/ora higher yield of high value chips when exposed on one particularexposure tool, while another mask may produce higher yield and/or valuewhen exposed on a different exposure tool. Given the limited amount ofdata available on CD distributions on the mask, the limited dataavailable on the full pattern transfer process across the exposurefield, and the difficulty in determining exactly which patterns vary onthe chips to make the chips produced by one tool perform better thanthose produced by another tool, it is usually not possible to determinewhy one mask/tool combination should perform better than another.

The lithography process simulation and metrology method using thesignature patterns described herein can enable a better understanding ofthe contribution of mask variations to CD and yield performance, andthereby facilitate improved MBAPC control. By determining the variationsnot just in the CD distribution of the metrology targets but of theprocess condition parameters, using the signature patterns to determinechanges in the process condition parameters allows for fasteridentification of specific masks where the performance of the mask interms of CD distributions, CD behavior of specific features, or specificmetrics of pattern transfer does not meet expectations. As an example,and by illustration only, assume that a series of masks is exposed onthe same exposure tool in succession and the metrology results collectedon a set of signature patterns which may or may not be common to thedifferent masks, but signature patterns produce the same calculatedchanges in process condition parameters. If the changes in processcondition parameters for one of the masks is noticeably different thanthe changes in process condition parameters for the other masks, giventhat there was little time for the exposure tool to drift between theexposures, this would serve as a quick and effective identification of aproblem with the quality of the one mask with different changes inprocess condition parameters. More information could then be collectedfor that mask to determine if it should be used in production or not,and, if it is to be used, a full chip simulation could help identifymask specific offsets in the process condition parameters to be appliedto the exposures using that mask to produce the optimum CD and patterntransfer performance at the wafer level.

Using the signature patterns to determine changes in the processcondition parameters is especially useful for products that areprocessed on an infrequent basis. In a conventional process controlsystem, these infrequent lots would be exposed using a time weightedaverage of the previous exposure doses used for the same product/layeron a given exposure tool, even though there may have been extensivechanges to the tool since the last time a lot of that product/layer wasprocessed. In more advanced approaches, the exposure dose may beadjusted based on the average change of all other recently processedlots of similar product/layers on the same tool. However, the choice ofwhat constitutes a “similar” layer is often based on empirical results(a limited history showing similar CDs at similar times in the past), ora subjective sense on the part of the person directing the productionflow or writing the rules for an automated flow control system. Oftenthe creation of groups of “similar” product layers is determined basedon the design rules or technology used for the particular design with noknowledge of how the mask manufacturing errors differ from mask to mask.

Using the signature patterns to determine changes in the processcondition parameters can improve these process control approaches in anumber of ways. First, the question of what constitutes a “similar”product/layer can be answered rigorously, not subjectively. Using thefull chip simulation system and the signature patterns, incoming circuitdesigns could be analyzed and automatically rated on their sensitivityto variations of the different process condition parameters. Designswhose simulated metrics have similar sensitivities to the processcondition parameters could be grouped together and SPC or APC controlalgorithms could be used to treat them as a group. In this manner, evenif a particular mask that belongs to such a group is not used for aperiod of time, the optimum process conditions to use for that maskwould still be known from the performance of the other members of thegroup. In addition, above and beyond the improvement obtained by betterclassification into similar groups, the corrections to be applied to theexposure tool would be greatly improved because the user could determinenot just a time averaged exposure dose offset, but an exposure doseoffset appropriate to the known changes in the full range of processcondition parameters determined from all other products that had beenprocessed since the last time the particular mask in question had beenused. Thus, if a particular aberration of the projection lens orillumination system had been known to have drifted, a full chipsimulation or a critical portion thereof could be quickly recomputed andnew optimum settings of the adjustable process condition parameters canbe determined based on the complete configuration of all of the processcondition parameters.

The process condition parameters computed from measured changes inmetrics associated with the signature patterns can also be used to feedforward corrections to an etch process or other subsequent processsteps. For example, even if the measured and/or simulated CDs are withinspecification, if the slope of the resist features is incorrect due tofocus offsets or other aberrations, the final width of the etchedstructure will be incorrect as well. Although an out-of-specificationresist pattern on a wafer can be cleaned (stripped) and reworked, oncethe pattern is etched into the underlying layer, no further correctionis possible, and the entire wafer, possibly even the entire wafer lot,may have lower than acceptable yield and may even need to be scrapped.For this reason, feed forward corrections to the etch process are veryvaluable.

The process condition parameters computed from measured changes inmetrics associated with the signature patterns allow the lithographyprocess control system to comprehend not just the resist CDs, but alsohow those CDs will transfer through the etch process. The correlationbetween focus and resist sidewall angle, and the correlation betweenresist sidewall angle and the final etched CD, is well known, and can bepredicted by empirical lookup tables or models of the etch process thatare well documented in the literature. The full chip simulation systemdisclosed in U.S. Pat. No. 7,003,758 and described below in conjunctionwith FIG. 13 is well suited to perform etch simulation as well aslithography simulation, and can be used to predict, optimize and adjustthe etch process to deliver the optimum final etched CD distributionacross the entire chip; or, failing to be able to correct thelithography process adequately, to signal an alarm and prevent the lotof wafers from being etched.

FIG. 13 is a diagram of one embodiment of a lithography simulationsystem 1300, according to the invention. System 1300 includes, but isnot limited to, one or more general purpose-type computing systems,including but not limited to, an application processing system 1314 aand a front-end processing system 1314 b. Application processing system1314 a is suitably configured to handle job management of the overalloperations of system 1300. In particular, in one embodiment, applicationprocessing system 1314 a includes an application processing device 1336and an application SCSI RAID 1338 a. Application processing device 1336is suitably programmed to provide management of the operations of thevarious components of system 1300. In this regard, for example,application processing device 1336 may be programmed to partition adesign database for the various components of an accelerator system1316, thereby specifying the individual jobs, functions or processesperformed by components of accelerator system 1316. Application SCSIRAID hard-disk array 1338 a provides storage for the programs and data(for example, design database) used by application processing device1336.

Front-end processing system 1314 b includes a front-end processingdevice 1340 which is suitably programmed to handle or perform directinteraction with the user or operator (i.e., the “outside world”) via,for example, client computer(s) (not illustrated) that provide operatoror user access to system 1300 for job setup and/or resultsreview/analysis. A front-end SCSI RAID hard-disk array 1338 b,associated with front-end processing device 1340 should be a highcapacity storage device, since front-end SCSI RAID 1338 b is used tostore results and images of many simulation jobs. Front-end processingsystem 1314 b also communicates with application processing system 1314a, to provide or retrieve data to or from application SCSI RAID 1338 a(for example, the design database), and instructs application processingsystem 1314 a to start a job, as instructed by the user or operator.

Application processing system 1314 a and front-end processing system1314 b connect with accelerator system 1316, for example, through highspeed switches (for example, gigabit-Ethernet switches 1342 a and 1342b). Switches 1342 a and 1342 b may be Dell 5224 Power Connect,manufactured and provided by Dell Computer (Austin, Tex., USA). Theimplementation and operation of the Dell 5224 Power Connect aredescribed in detail in application notes, technical/journal articles anddata sheets, all of which are incorporated by reference herein.

In one embodiment, all or substantially all of the actualcomputationally intensive tasks of lithography simulation may beconducted by accelerator system 1316, and, in particular, one or moreaccelerator components 1316 a-n. This architecture allows scalablecomputation capacity, by changing the number of accelerator hardwarecomponents 1316 a-n. Moreover, this architecture also enables orenhances overall fault-tolerance of system 1300. For example, should agiven accelerator hardware component 1316 a-n fail, its jobs may bere-assigned to the other accelerator hardware components 1316 a-n, and,in this way, system 1300 maintains its operational condition/state.

In particular, accelerator system 1316 may include one or moreaccelerator components 1316 a-n, each having one of microprocessorsubsystem 1344 a-n (including one or more microprocessors), one or moreaccelerator subsystems 1346 a-n, and local or resident memory storage1348 a-n coupled to an associated microprocessor subsystem 1344 a-n. Theextent or amount of hardware acceleration capability may be balancedwith microprocessor subsystems 1344 a-n, depending on the extent oramount of computation to be performed.

In one embodiment, microprocessor subsystems 1344 a-n each includes twoXeon microprocessors manufactured by Intel (Santa Clara, Calif., USA).The accelerator subsystems 1346 a-n each includes a plurality ofApplication-Specific Integrated Circuit (ASIC), special-purpose DSPintegrated circuits, and/or programmable gate arrays (for example,field-programmable gate arrays (“FPGAs”)). In fact, each of acceleratorsubsystems 1346 a-n may include multiple accelerator subsystems, forexample, accelerator subsystem 1346 a may include all the acceleratorsubsystems 1346 a 1-6 ax, as illustrated in FIG. 13. In this way, whenfully utilized, each of accelerator subsystems 1346 a-n containscomputational capacity of roughly twenty-five Xeon microprocessors.

A bus 1350 a-n facilitates high-speed communication betweenmicroprocessor subsystem 1344 a-n and associated acceleratorsubsystem(s) 1346 a-n. The communication protocols and techniques on bus1350 a-n may be PCI, PCIX, or other high-speed communication protocolsand techniques. Indeed, any high-speed technique, whether now known orlater developed, may be implemented over bus 1350 a-n. Notably, in oneembodiment, the bus interface may be implemented using a 21P100BGC PCI-Xbridge (64 bit/133 MHz) from International Business Machines Corporation(Armonk, N.Y., USA). The implementation and operation of the 21 P100BGCare described in detail in application notes, technical/journal articlesand data sheets, all of which are incorporated by reference herein.

In one embodiment, computations that are performed by be acceleratorsubsystems 1346 a-n include, for example, anti-aliasing filtering anddown-sampling, Fast Fourier Transforms for aerial image computation,image filtering and/or thresholding operations in resist modeling. Thecomputations that are performed by microprocessor subsystems 1344 a-ninclude, for example, polygon-to-binary bitmap conversion andapplication programs or processes (e.g., RET verification). Thepartitioning of computing tasks between microprocessor subsystem 1344a-n and accelerator subsystems 1346 a-n is application-dependent and mayvary from application to application or job to job.

The invention has been described above with reference to specificembodiments. It will, however, be evident that various modifications andchanges may be made thereto without departing from the broader spiritand scope of the invention as set forth in the appended claims. Theforegoing description and drawings are, accordingly, to be regarded inan illustrative rather than a restrictive sense.

1. A set of process window signature patterns, comprising: a pluralityof patterns in a circuit device area of a mask, wherein a collectiveresponse of the plurality of patterns to a lithography process uniquelyidentifies any deviation from nominal in process condition parameters ofthe lithography process.
 2. A set of process window signature patternsas set forth in claim 1, wherein the patterns in the set are obtained byperforming simulations of the lithography process, using arepresentation of the device area of a mask, at two or more non-nominallocations and at nominal condition in a process window to producesimulation results.
 3. A set of process window signature patterns as setforth in claim 2, wherein the patterns in the set are further obtainedby determining simulated values from the simulation results for at leastone metric at the two or more non-nominal locations and at nominalcondition in the process window, wherein the simulated values for the atleast one metric are determined at a plurality of locations within thedevice area.
 4. A set of process window signature patterns as set forthin claim 3, wherein the patterns in the set are further obtained by, foreach of the plurality of locations within the device area, determining adifference between simulated values for the at least one metric at thetwo or more non-nominal locations in the process window and thesimulated value for the at least one metric at nominal condition.
 5. Aset of process window signature patterns as set forth in claim 4,wherein the patterns in the set are further obtained by identifying anumber of locations within the device area where the difference for theat least one metric is above a threshold.
 6. A set of process windowsignature patterns as set forth in claim 5, wherein the patterns in theset are further obtained by, if the number of identified locations isgreater than a predetermined number, modifying the threshold andidentifying the locations within the device area where the differencefor the at least one metric is above the threshold until the number ofidentified locations within the device area is less than thepredetermined number.
 7. A set of process window signature patterns asset forth in claim 6, wherein the patterns in the set are furtherobtained by assigning patterns associated with the number of identifiedlocations within the device area that is less than the predeterminednumber to be signature patterns.
 8. The set of process window signaturepatterns set forth in claim 2, wherein the process window is defined byat least two process condition parameters of the lithography process. 9.The set of process window signature patterns set forth in claim 8,wherein the at least two process condition parameters are focus andexposure dose.
 10. The set of process window signature patterns setforth in claim 8, wherein the at least two process condition parametersare selected from the group consisting of focus, exposure dose,numerical aperture, film stacks, lens aberrations, coherence, andillumination shifts.
 11. The set of process window signature patternsset forth in claim 7, wherein the patterns in the set are furtherobtained by: obtaining measured values of the at least one metric forthe signature patterns, the measured values being obtained frommeasurements of the signature patterns printed on a wafer processed bythe lithography process using the mask; finding a location in theprocess window where a total difference measure between the measuredvalues of the at least one metric for all of the signature patterns andthe simulated values of the at least one metric for all of the signaturepatterns at the location in the process window is minimized; anddetermining a change between the location in the process window wherethe total difference measure is minimized and nominal condition todetermine a change in the lithography process.
 12. The set of processwindow signature patterns as set forth in claim 3, wherein a matrix isgenerated representing sensitivity of the at least one metric todeviation from nominal in various process condition parameters.
 13. Aset of process window signature patterns selected from a design layoutof a mask, wherein the set of patterns is located in a device area ofthe design layout, and wherein the set of patterns is used to generateone or more metrics characterizing a lithography process, such that thepatterns and the metrics are paired, and sensitivity of thepattern-metric pairs to various process condition parameters is used tomonitor deviation of the lithography process from a desired processcondition.
 14. The set of process window signature patterns as set forthin claim 13, wherein a sensitivity matrix is generated representingcorrelation of the pattern-metric pairs to various process conditionparameters.
 15. The set of process window signature patterns as setforth in claim 14, wherein a subset of process window signature patternsis identified by iteratively refining the sensitivity matrix throughsimulation, thereby choosing the patterns whose correspondingpattern-metric pairs show decoupled sensitivity to various inputvariables of the matrix, the input variables representing the variousprocess condition parameters.
 16. The set of process window signaturepatterns as set forth in claim 15, wherein number of process conditionparameters is optimized based on the iterative refinement of thesensitivity matrix.
 17. The set of process window signature patterns asset forth in claim 13, wherein the patterns are selected from the entiredesign layout of the mask, or a portion thereof.
 18. The set of processwindow signature patterns as set forth in claim 13, wherein the responsefrom the patterns is fed forward in a process control loop to performone or both of: compensation for deviation of the lithography processfrom a desired process condition, and, failure analysis.
 19. The set ofprocess window signature patterns as set forth in claim 13, wherein ametrology recipe is chosen based on one or more attributes of a pattern,wherein the metrology recipe is used to print the design layout on awafer using the lithography process, and to collect response to generatethe one or more metrics.
 20. The set of process window signaturepatterns as set forth in claim 19, wherein the attributes of thepatterns include one or more of: location of a pattern, shape of apattern, size of a pattern, and, wafer layer where a pattern belongs.21. The set of process window signature patterns as set forth in claim13, wherein the process condition parameters include one or more of:focus, exposure dose, film stacks, numerical aperture, lens aberrations,coherence, and, illumination shift.